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The network of bio-intelligence Physarum polycephalum (slime mould) is eroded into a post-natural landscape that reaches potential areas for bio-material algae photosynthetic processing in London. Machine Learning CycleGAN elaborates the blending of London satellite maps and networks structures extracted from experiments with slime mould. Reflecting on environmental deterioration caused by fossil fuels, this project reveals the prospect of a post-anthropocentric city, which is built by humans, and planned by slime mould and algae. This is a place where humans and nonhumans become intertwined.
It reveals the prospect of a post-anthropocentric city, which is built by humans, planned by slime mould and sufficed by algae, where human and nonhuman are intertwined.
Bio-intelligence slime mould endeavours to find untapped resources for photosynthetic process of bio-material algae in city's and expand the landscape in a most efficient way to minimise unnecessary expenditure.
Bio-intelligence slime mould endeavours to find untapped resources for photosynthetic process of bio-material algae in city's and expand the landscape in a most efficient way to minimise unnecessary expenditure.
The urban system: from top to bottom, in scales, elements and programs.
Proximity algorithms helps generate areas which boost photosynthetic process for biomaterial algae. Bio-intelligence slime mould will create a network connecting these areas into this datascape.
By capturing aspects of the bio-intelligence of slime mould (Physarum polycephalum), in terms of communication, distribution and optimisation, we see a great opportunity for co-evolution.
CycleGAN helps to blend images, from slime mould to terrain, terrain to urban landscape.
The urban system is transiting from top to bottom, in scales, elements and programs.